Detection of Electric Network Frequency in Audio Using Multi-HCNet
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| Publicat a: | Sensors vol. 25, no. 12 (2025), p. 3697-3720 |
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| Autor principal: | |
| Altres autors: | , , , , , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3223942199 | ||
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| 022 | |a 1424-8220 | ||
| 024 | 7 | |a 10.3390/s25123697 |2 doi | |
| 035 | |a 3223942199 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Li, Yujin | |
| 245 | 1 | |a Detection of Electric Network Frequency in Audio Using Multi-HCNet | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the increasing application of electrical network frequency (ENF) in forensic audio and video analysis, ENF signal detection has emerged as a critical technology. However, high-pass filtering operations commonly employed in modern communication scenarios, while effectively removing infrasound to enhance communication quality at reduced costs, result in a substantial loss of fundamental frequency information, thereby degrading the performance of existing detection methods. To tackle this issue, this paper introduces Multi-HCNet, an innovative deep learning model specifically tailored for ENF signal detection in high-pass filtered environments. Specifically, the model incorporates an array of high-order harmonic filters (AFB), which compensates for the loss of fundamental frequency by capturing high-order harmonic components. Additionally, a grouped multi-channel adaptive attention mechanism (GMCAA) is proposed to precisely distinguish between multiple frequency signals, demonstrating particular effectiveness in differentiating between 50 Hz and 60 Hz fundamental frequency signals. Furthermore, a sine activation function (SAF) is utilized to better align with the periodic nature of ENF signals, enhancing the model’s capacity to capture periodic oscillations. Experimental results indicate that after hyperparameter optimization, Multi-HCNet exhibits superior performance across various experimental conditions. Compared to existing approaches, this study not only significantly improves the detection accuracy of ENF signals in complex environments, achieving a peak accuracy of 98.84%, but also maintains an average detection accuracy exceeding 80% under high-pass filtering conditions. These findings demonstrate that even in scenarios where fundamental frequency information is lost, the model remains capable of effectively detecting ENF signals, offering a novel solution for ENF signal detection under extreme conditions of fundamental frequency absence. Moreover, this study successfully distinguishes between 50 Hz and 60 Hz fundamental frequency signals, providing robust support for the practical deployment and extension of ENF signal applications. | |
| 653 | |a Forgery | ||
| 653 | |a Machine learning | ||
| 653 | |a Design | ||
| 653 | |a Audio recordings | ||
| 653 | |a Accuracy | ||
| 653 | |a Usability | ||
| 653 | |a Deep learning | ||
| 653 | |a Forensic sciences | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Multimedia | ||
| 653 | |a Neural networks | ||
| 653 | |a Signal processing | ||
| 700 | 1 | |a Lu Tianliang | |
| 700 | 1 | |a Peng Shufan | |
| 700 | 1 | |a He Chunhao | |
| 700 | 1 | |a Zhao, Kai | |
| 700 | 1 | |a Yang, Gang | |
| 700 | 1 | |a Chen, Yan | |
| 773 | 0 | |t Sensors |g vol. 25, no. 12 (2025), p. 3697-3720 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223942199/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223942199/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223942199/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |